Bhosale et al. BMC Plant Biology 2012, 12:32
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RESEARCH ARTICLE
Open Access
Association analysis of photoperiodic flowering
time genes in west and central African sorghum
[Sorghum bicolor (L.) Moench]
Sankalp U Bhosale1, Benjamin Stich2, H Frederick W Rattunde3, Eva Weltzien3, Bettina IG Haussmann1,4*,
C Thomas Hash4,5, Punna Ramu5, Hugo E Cuevas6,7, Andrew H Paterson6, Albrecht E Melchinger1 and
Heiko K Parzies1
Abstract
Background: Photoperiod-sensitive flowering is a key adaptive trait for sorghum (Sorghum bicolor) in West and
Central Africa. In this study we performed an association analysis to investigate the effect of polymorphisms within
the genes putatively related to variation in flowering time on photoperiod-sensitive flowering in sorghum. For this
purpose a genetically characterized panel of 219 sorghum accessions from West and Central Africa was evaluated
for their photoperiod response index (PRI) based on two sowing dates under field conditions.
Results: Sorghum accessions used in our study were genotyped for single nucleotide polymorphisms (SNPs) in six
genes putatively involved in the photoperiodic control of flowering time. Applying a mixed model approach and
previously-determined population structure parameters to these candidate genes, we found significant associations
between several SNPs with PRI for the genes CRYPTOCHROME 1 (CRY1-b1) and GIGANTEA (GI).
Conclusions: The negative values of Tajima’s D, found for the genes of our study, suggested that purifying
selection has acted on genes involved in photoperiodic control of flowering time in sorghum. The SNP markers of
our study that showed significant associations with PRI can be used to create functional markers to serve as
important tools for marker-assisted selection of photoperiod-sensitive cultivars in sorghum.
Background
Sorghum [Sorghum bicolor (L.) Moench] is a major staple
crop and source of income for millions of people in Western and Central Africa (WCA). The success of sorghum
production is determined to a considerable extent by the
appropriateness of the flowering time for the specific production environment. The highly variable sowing dates,
due in part to erratic onset of the rainy season, present an
important challenge since grain maturity needs to occur at
a more fixed calendar date to coincide with the end of the
rainy period for successful grain filling and pest avoidance
[1]. Thus, photoperiod-sensitive flowering responses of
sorghum in WCA enhance adaptation by enabling more
fixed maturity dates despite variable sowing dates [2-4].
* Correspondence: bettina.haussmann@uni-hohenheim.de
1
Institute of Plant Breeding, Seed Science, and Population Genetics,
University of Hohenheim, 70593 Stuttgart, Germany
Full list of author information is available at the end of the article
The transition of plant growth from vegetative to generative stage is the primary determinant of flowering time
in crops of determinant growth type such as sorghum.
The degree to which varieties can adjust this onset of
panicle initiation with differing sowing dates, and photoperiod conditions, is called photoperiodic flowering
response [5]. Photoperiod sensitivity triggers panicle initiation in short-day (SD) plants such as sorghum when they
sense an appropriate decrease in day length [6].
The molecular basis of flowering time has been extensively studied in Arabidopsis thaliana where mutant
plants with an altered flowering phenotype were analyzed
for their flowering behavior under laboratory conditions.
As a result, four important pathways regulating floral
induction have been identified: the photoperiod (longday (LD) promotion) pathway, gibberellic-acid promotion
pathway, vernalization pathway, and autonomous pathway [7-9]. A basic understanding of the molecular complexity of flowering time in important agronomic species
© 2012 Bhosale et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Bhosale et al. BMC Plant Biology 2012, 12:32
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with large genomes such as maize (Zea mays L.), wheat
(Triticum aestivum L.), barley (Hordeum vulgare L.), and
pearl millet (Pennisetum glaucum (L.) R. Br.) has been
facilitated by comparative use of floral pathways from
A. thaliana (for review see, [9-11]). Flowering time genes
and sequences can be used by breeders for the development of molecular markers or for targeted genetic modification of flowering time.
Current knowledge on genetics of photoperiod-sensitive
flowering
Since Bünning [12] first proposed that the photoperiodic
time-keeping mechanism is associated with the circadian
clock, there has been a considerable amount of research
on the photoperiod pathway. The basis of day-length
measurement is the interaction of an external light signal
with the circadian rhythm [6]. In the photoperiod-sensitive flowering process (Figure 1), light signals are perceived by photoreceptors involved in the resetting of the
circadian clock, with the result that plants respond to the
light and dark cycles [13]. Genes such as CIRCADIAN
CLOCK ASSOCIATED1 (CCA1), LATE ELONGATED
Figure 1 A simplified model of flowering mediation by
photoperiod in Arabidopsis (modified from Izawa et al. [9]).
Page 2 of 10
HYPOCOTYL (LHY), and TIMING OF CAB EXPRESSION1 (TOC1) are the core components of the central
oscillator of the circadian system. The oscillator determines the phase of CONSTANS (CO) transcription [14].
CO is an important gene that links the circadian clock to
flowering [15], and it induces the transcription of FLOWERING LOCUS T (FT) to promote flowering [11,16].
Recent research has shown that the FT protein in Arabidopsis and corresponding proteins in other plants are an
important part of the florigen [17,18], which is a leaf-generated mobile flowering signal initiating floral morphogenesis at the shoot apex [17,19].
Status of research on flowering time genes in sorghum
A series of six maturity genes have been recognized to
affect flowering time and photoperiodic flowering
response in sorghum: Ma 1 , Ma 2 , Ma 3 , Ma 4 , Ma 5 , and
Ma6 [20,21]. The first four maturity genes inhibit flowering under LD conditions but allow early flowering under
short day conditions. Of these first four genes, Ma 1
causes the greatest sensitivity to LD conditions. In contrast, Ma2, Ma3 , and Ma4 generally have more modest
effects on sensitivity to LD conditions [20]. Kouressy et
al. [22] showed that photoperiod sensitivity was affected
by dominant alleles of one major gene, equivalent to the
Ma5 or Ma6 maturity loci identified by Aydin et al. [23].
Several other studies report on sorghum photoperiodic
flowering [24-26]. These studies highlighted the role of
phytochromes as an important gene family. Childs et al.
[27] demonstrated that the Ma3 gene is synonymous to
PHYB and sequenced other phytochromes such as PHYA
and PHYC. It is interesting that mutations in sorghum
Ma 3 and A. thaliana PHYB both reduce sensitivity to
non-inductive day-lengths [28,29]. Recently, positional
cloning identified Ma1 as a putative pseudoresponse regulator protein 37 (PRR37), which acts as inhibitor of CO
and floral activator genes [30]. Bhattramakki et al. [31]
reported that primers for SSR marker Xtxp320 are
derived from the PHYB sequence, but this sequence variation was not detected by White et al. [32] in studies of
the PHYB sequence from several diverse sorghum accessions. There are indications that the Xtxp320 (PHYB) primer pair detects more than one locus as there are reports
of Xtxp320 mapping to SBI-01 (where PHYA, PHYB and
PHYC are located) and/or SBI-10 (e.g., [33]). However, to
our knowledge, there has not been a study analyzing systematically the effect of candidate genes (CGs) from the
photoperiod sensitivity pathway on photoperiodic flowering response in sorghum.
Advanced plant breeding techniques such as MAS
have the potential to accelerate the selection process
substantially [34,35]. Functional markers are the stateof-the-art molecular tools that minimize the risk of
recombination between marker and QTL alleles [36].
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Page 3 of 10
Association studies based on linkage disequilibrium offer
a new possibility to identify marker-trait associations (cf.
[37]).
In this study we examined a panel of sorghum accessions from West Africa established expressly to represent
the range of photoperiodic response. The objectives of our
study were to (i) characterize the photoperiodic flowering
response of these sorghum accessions under field conditions, and (ii) investigate the association between variation
for photoperiodic sensitivity for flowering time and polymorphisms in six partially amplified genes putatively
related to variation in flowering time in sorghum [CRYPTOCHROME 1 (CRY1; Sb04g023680), CRYPTOCHROME
2 (CRY2; Sb06g018510), LATE ELONGATED HYPOCOTYL (LHY; Sb04g031590), GIGANTEA (GI; Sb03g003650),
HEADING DATE 6 (HD6; Sb02g001110), and Dwarf8
(SbD8; Sb01g010660)].
Results
Phenotypic evaluation
Analysis of the field data on flowering time showed that
sorghum accessions of our study exhibited a wide range of
photoperiodic response. The days to 50% flowering
(DFL50%) of sorghum accessions sown on 10 June ranged
from 47 to 141, and those sown on 10 July from 44 to 117
days, respectively (Table 1). The PRI for the accessions
ranged from -7 to 37 (see additional file 1). From the phenotypic data it was observed that when sown late (on 10
July) the accessions of sorghum generally showed a reduction of growth cycle compared to when sown earlier (on
10 June). The mean DFL50% values of the two sowing
dates were significantly different (p < 0.01). For both sowings dates, early-maturing accessions were generally less
sensitive to photoperiod (i.e. had lower PRI values), than
the late-maturing accessions (which had higher PRI
values). The mean plant height of the accessions in the
June sowing was significantly (p < 0.001) greater than their
mean plant height in the July sowing.
Co-localization of the genes on sorghum genome
sequence
Gene sequences of the CGs studied were BLASTsearched against the aligned sorghum genome sequence
(Paterson et al. [38]) to identify the physical locations of
these genes. BLAST search identified that the CRY1-b1
gene has its best possible hit on sorghum chromosome 4
at 53.35 Mb (similar to Sb04g023680), GI gene has a
unique location on sorghum chromosome 3 at 3.88 Mb
(similar to Sb03g003650), CRY2-2 gene has a location on
sorghum chromosome 6 at 48.11 Mb (similar to
Sb06g018510), LHY gene is located on sorghum chromosome 4 at 61.55 Mb (similar to Sb04g031590), HD6 gene
has its best possible hit on sorghum chromosome 2 at
0.98 Mb (similar to Sb02g001110), and SbD8 gene has its
best possible hit on sorghum chromosome 1at 9.42 Mb
(similar to Sb01g010660).
To validate whether these genomic regions have any
association with flowering genes like CRY1b and GI, gene
sequences for CRY1b and GI from different cereals and
other model crop species [CRY1b sequences from Oryza
sativa (OsAB073547), T. aestivum (TaEF601537), H. vulgare (HvDQ201153), A. thaliana (AtGQ177026), and GI
sequences from O. sativa (OsAJ133787), T. aestivum
(TaAF543844), H. vulgare (HvAY740524), A. thaliana
(AtAF105064)] were BLAST searched against the aligned
sorghum genome. For each gene, only the best hit could
be considered for budget reasons. For the six CGs, an
overview of their BLAST scores, E-values with sorghum,
and percentage similarity with their respective homologs
in Arabidopsis and rice obtained by direct nucleotide
sequence comparisons is given in Table 2.
Candidate gene sequence diversity
Sequences obtained from primers designed in this study
(Table 3) were the desired fragments of the targeted gene
(see additional file 2). This was confirmed by the high
BLAST scores obtained when all fragments were
searched against the sorghum genome sequence database
(Phytozome) using the BLAST tool. For the CGs, 35% of
the total sequenced region (4386 bp) was coding and 65%
was non-coding. The number of polymorphic sites was
highest for GI and lowest for CRY2-2 (Table 4). Considering all six genes in this study, the average number of
polymorphic sites (S) was 12.5, the average nucleotide
diversity (π) was 0.005, and Tajima’s D value was negative
for all genes and was highly significant for genes CRY2-2,
HD6, and GI.
Linkage disequilibrium analyses
Table 1 Days to 50% flowering (DFL50%) and plant
height (cm) of sorghum accessions for two sowing dates
Sowing 1 (June 10)
Sowing 2 (July 10)
Trait
Range
Mean
SE
Range
Mean
SE
DFL50%
47-141
99.84
1.05 44-117
79.00
0.76 40.23**
Plant height 132-590
417.74 5.20 112-550
t
362.86 4.91 8.51***
**, *** Genetic differences among accessions significant at P < 0.01 and <
0.001, respectively
A linkage disequilibrium analysis was performed for six
CGs under study. The average r 2 values for the CGs
were, CRY1-b1 = 0.21, CRY2-2 = 0.13, LHY-4 = 0.074,
HD6 = 0.31, GI = 0.17, and SbD8 = 0.024. In the case of
the CRY1-b1 gene, two strong linkage disequilibrium
blocks were detected at the 5’ UTR (untranslated region)
and at the 3’ end of the sequence (coding region). The
linkage disequilibrium matrix plots for the CGs studied
are shown in Figure 2 and additional file 3.
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Table 2 Sorghum candidate genes studied, their predicted and amplified sizes in base pairs,% of gene targeted,
BLAST scores and E values with sorghum, their percentage similarities with homologous loci in Arabidopsis thaliana
(At) and rice (Os) obtained by direct nucleotide sequence comparisons
Gene
Predicted
size (bp)
Fragment
size (bp)
% of gene
targeted
BLAST score
sorghum
E value
Similarity
Arabidopsis %
Homologous
At locus
Similarity
rice %
Homologous
Os locus
CRY1-b1
3954
726
18
1236.4
0
38
NM_116961
50
AB073546
CRY2-2
3971
657
17
1160.9
0
57
AY05744
53
AJ298877
SbD8
2653
531
27
910.0
0
56
NM_105306
82
AB262980
Hd6
6454
807
13
425.1
6.2e-117
46
ATHCK2B
52
AB036788
GI
8589
960
11
1703.1
0
57
NM_102124
83
AJ133787
LHY-4
2110
706
25
1322.0
0
37
NM_001197953
65
NM_001067567
Association analyses with candidate genes
Association analyses were performed for all polymorphic sites in all six genes sequenced. Significant (p
= 0.05) associations were found between PRI and several polymorphic sites within CGs CRY1-b1 and GI
(Table 5 and additional file 4). The SNP722 in CRY1b1 (change of nucleotide base from T to A) and
SNP888 in GI (change of nucleotide base from T to C)
showed effects on PRI of -4.2 and +8 days, respectively.
A negative effect on PRI means that the difference in
flowering time between the June and July sowing dates
was reduced (i.e., photoperiod sensitivity is reduced),
whereas a positive effect on PRI indicates that the difference in flowering time was increased (i.e., photoperiod sensitivity is increased).
Discussion
Photoperiod sensitivity
The variability for photoperiod sensitivity observed in this
panel of sorghum accessions was very large, ranging from
highly insensitive varieties (no change in vegetative period)
to highly sensitive (with a 37-day reduction in vegetative
cycle induced by the 30-day delay in sowing: from 10 June
to 10 July). The accessions that matured earlier in both
sowings were mostly the least photoperiod-sensitive ones
Table 3 Sorghum candidate genes studied, their primer
sequences, and primer melting temperatures (Tm)
Candidate genes
Tm
Forward primer sequences (5’®3’)
Reverse primer sequences (5’®3’)
CRY1-b1
58°C
60°C
54°C
56°C
52°C
48°C
ACAACCCAGACTCGCATAG
GAGGGATCGAACCGTAGAG
ACCTTGTTTCTCCGTTCC
CTTCTTGCAGTCTGGCTTT
CCCTTGACATTGACATAC
CATTGATTCCCACTTGA
HD6
58°C
64°C
GATTACTGCCATTCACAAGG
GAAGCTCAGGWCCCTTGAAGTA
GI
58°C
58°C
TCCGCTTCAGCCACCTAC
CTGCCAGAGCAATGAGACAA
SbD8
60°C
54°C
GACGACAAGGATGAGGAGC
CGAGGTGGCGATGAGC
CRY2-2
LHY-4
(having lower PRI values, see additional file 1). Earlierflowering accessions made the transition from vegetative
growth to generative growth before the day-length reached
the critical photoperiods required to induce flowering in
the later-flowering photoperiod-sensitive accessions. For
accessions flowering comparatively late in the June planting, the critical photoperiod significantly reduced flowering time when they were sown under decreasing daylength conditions in July. This was clearly demonstrated
by significantly lower mean DFL50% of the July sowing
compared to the mean DFL50% of June sowing in these
late, photoperiod-sensitive accessions. This reduction in
mean DFL50% comes with its consequence, as the mean
plant height of the accessions in the July sowing was significantly lower than their mean plant height in the June
sowing. Similar observations on reduction in vegetative
growth resulting from decreasing day-length conditions
were made by Folliard et al. [39] on a guinea sorghum cultivar, where total number of leaves was reduced by half
when it was sown at four different sowing dates. The
diversity of photoperiod response of our panel of accessions made it an appropriate choice for association analysis for candidate flowering genes.
Linkage disequilibrium analyses
The linkage disequilibrium measure r 2 ranged from
0.024 to 0.21 for the CGs in our study. The mean r2 of
0.18 was comparable to the study on sorghum [40]
reporting r2 > 0.1 but lower than the previous study on
barley [41] which reported r 2 > 0.4. The variability in
the range of r2 estimates observed in our study can be
due to the fact that linkage disequilibrium estimates
vary according to the target genomic region as well as
number of polymorphic sites [42,43]. Furthermore,
because of limited coverage (small fragment size) of the
CGs studied, it seems inappropriate to describe the
decay of linkage disequilibrium along each CG. Full
length sequencing of the studied and additional important photoperiod CGs will be necessary to describe patterns of linkage disequilibrium in the sorghum flowering
time gene network.
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Table 4 Sequence diversity of genes CRY1-b1, CRY2-2, SbD8, HD6, GI, and LHY-4 in sorghum
Fragment size (bp)
Predicted gene size (bp)
% gene targeted
S
π
CRY1-b1
726
3954
18
15
0.002
-1.50
CRY2-2
657
3971
17
2
0.001
-2.67**
SbD8
528
2653
20
4
0.002
-1.27
HD6
804
6454
13
6
0.011
-2.62***
GI
960
8589
11
42
0.001
-2.72***
LHY-4
706
2110
34
6
0.012
-1.75
Gene
Tajima’s D
For each gene, the total predicted size and the size of amplified fragment, S number of polymorphic sites, π the pairwise nucleotide diversity and Tajima’s D
value are reported; **, *** significant at P < 0.01 and < 0.001, respectively
Population structure and association analysis
The CGs chosen for this study were selected on the
basis of comparative genomic studies on photoperiodic
flowering time genes in A. thaliana and rice [44-46].
From these studies it was evident that the respective
genes have a high degree of similarity in structure and
function between the latter two species.
Using software STRUCTURE to infer population structure, and employing Evanno’s method to estimate the
number of subgroups, our sorghum germplasm panel
could be divided into two subgroups [47]. Race as well as
the geographical origin of the accessions, played a role in
the population structure of these sorghum accessions. We
used a mixed-model method for association analysis that
takes into account population structure as well as kinship
information. This model has proven to yield better results
in association studies compared to models ignoring these
factors [48,49]. The marker-phenotype association analysis
was carried out using values of PRI for each accession.
The field experiments were conducted in one year (2007)
and at one location. Significant genotype × year interactions for measures of photoperiod-sensitive flowering
response might occur in multi-location trials over years;
however, the sorghum accessions of our study have been
observed previously for their photoperiodic behavior over
years and therefore some background information on their
photoperiod response was known.
Out of the six genes analyzed, we detected in fragments
of genes CRY1-b1 and GI several polymorphic sites that
were significantly (p < 0.05) associated with PRI variation
in our sorghum panel. The first two candidate genes considered in our study were CRY1-b1 and CRY2-2. In
plants, cryptochromes and phototropins [50] are the two
types of blue light/UV-A receptors important for plant
photomorphogenesis. In A. thaliana, CRY1 mainly functions in de-etiolation [51], while CRY2 plays a role in the
regulation of photoperiodic flowering [52]. Hirose et al.
[53] showed that over-expression of OsCRY1 in rice
resulted in enhanced responsiveness to blue light, suggesting that OsCRY1 is a regulator of photomorphogenesis, similar to AtCRY1. Like AtCRY2, OsCRY2 is also
involved in the promotion of flowering time in rice [53].
But it was also shown that sub-cellular localization of
AtCRY2 does not change in response to blue light [54].
In our analysis, we did not find any significant associations of the CRY2-2 gene with PRI but several polymorphisms in the CRY1-b1 gene were significantly
associated with PRI, where the most important polymorphisms showed an effect on PRI value of up to -4.2
days (Table 5).
The CRY1 gene sequence in sorghum (SbCRY1) has
three important domains namely i) DNA photolyase binding a light harvesting cofactor [54], ii) FAD (flavin
adenine dinucleotide) binding domain of the DNA
photolyase - involved in energy harness of blue light [55],
and iii) blue/ultraviolet-sensing protein C terminal - this
domain is found in association with two previous
domains in eukaryotes [56]. Our BLAST results showed
that the CRY1-b1 gene fragment that we analyzed was
located between the first domain (DNA photolyase) and
the beginning of second domain (FAD domain) of the
SbCRY1 gene. The SNP at position 722 in CRY1-b1 was
therefore located in the domain of the DNA photolyase
located at the N-terminal domain of SbCRY1. In A. thaliana, it was shown that the N-terminal domain of the
CRY1 gene was essential for blue light reception [57].
This domain catalyzes the repair of photo-damage to the
light-harvesting apparatus resulting from ultraviolet irradiation. Photolyases and cryptochromes are related flavoproteins that bind FAD. Photolyases harness the energy
of blue light and cryptochromes (CRY1 and CRY2) mediate blue light-induced gene expression [58]. Therefore
the effect of the SNP at position 722 at the N-terminal in
our SbCRY1-b1 sequence suggested that the change in
nucleotide base from T to A (Table 5) might play an
important role in blue light reception in sorghum. This
observation can be supported by the fact that in wheat
the N-terminal domain of TaCRY1 contains a sequence
signal important for its nuclear export. Therefore, a
detailed analysis of SbCRY1 comparing its N-terminal
domain with its C-terminal domain might reveal their
exact roles in photomorphogenesis.
In addition to CRY1-b1, we found several polymorphic
sites in the sorghum GI gene homolog to be significantly
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CRY1-b1
GI
Figure 2 Strength and extent of linkage disequilibrium for genes CRY1-b1, and GI. Each point in the linkage disequilibrium matrix
represents a comparison between a pair of polymorphic sites, with the r2 values displayed above the diagonal, and P values for Fisher’s exact
test below.
associated with PRI, with the largest effect on PRI of
about 8 days (Table 5). Hayama et al. ([59,60]) reported
that in rice, rather than promoting flowering, OsGI
expression results in the suppression of flowering under
LD. It has been proposed that genetic mechanisms of
photoperiodic control in rice are similar to those in A.
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Table 5 Significant (P < 0.05) marker-phenotype associations for genes CRY1-b1 and GI in sorghum
Gene
CRY1-b1
Polymorphism
Change of allele state
Type of change
AE (days)
SE (AE)
P
SNP722
T_A
aa
-4.20
1.51
0.006
GI
SNP888
T_C
aa
+7.98
2.90
0.008
GI
indel904
0_1
fs
+7.25
2.64
0.008
GI
SNP909
C_G
aa
+7.38
2.07
0.001
Note: For change of allele state, 0 denotes the absence and 1 denotes the presence of an indel.
Type of change caused by polymorphism as aa: amino acid substitution or fs: frame-shift mutation, AE: allele effect, SE: standard error of the allelic effect, P: Pvalue of allelic effect
thaliana, but vary in downstream signaling of GI, at the
regulation of FT. In LD conditions, CO promotes flowering through FT activation in A. thaliana and conversely represses FT and flowering in rice, which is a SD
plant [60]. Similar to the observations in rice [59,60],
the positive allele effect on PRI observed in this study
(Table 5) indicates that SbGI enhances photoperiodic
response to SD conditions in sorghum, i.e., SbGI shortens the time to sorghum flowering in the later July sowing which is more exposed to SD conditions, while in
the June sowing (initially more exposed to LD conditions), SbGI delays sorghum flowering. Therefore,
detailed investigation by comparison of accessions
grown under SD and LD conditions would be necessary
to determine the exact mode of action of the GI gene
homolog in sorghum. Tajima’s D values for the gene fragments sequenced in our study were negative (Table 4)
with three genes (CRY2-2, HD6, and GI) having significantly negative values. Possible reasons for negative Tajima’s D values (obvious through large numbers of low
frequency variants) may be, firstly, that the sorghum
accessions of our study originated from different geographical regions and had little common history. Secondly,
it has been suggested that population structure existing
among the ancestral populations as a result of multiple
domestications and introgressions from wild relatives
could give rise to negative Tajima’s D values (see [61]).
Thirdly, the negative Tajima’s D values might indicate
that the gene fragments used in our study may have been
subjected to adaptive selection as variation in flowering
time may confer adaptive advantages in sorghum (see
[62]).
Conclusions
When looking at the flowering time gene network as a
whole, purifying selection is found in both coding and
non-coding DNA regions [63,64]. The sorghum sequence
dataset of our study is in agreement with this observation. Certainly, conclusions can be drawn from Tajima’s
D estimates found in our study about natural selection
affecting the studied sorghum genomic regions. However,
in our study the number of genes as well as the size of
the each CG fragment studied was small for effectively
capturing the signature of selection on photoperiodic
flowering time genes. It will be necessary to characterize
the entire flowering time gene network in sorghum to
know how selection has shaped the photoperiod pathway
of flowering time and thus helped sorghum to adapt to
climatic zones with different day-length conditions.
To utilize the SNPs identified to be significantly associated with PRI in our study, molecular markers could
be designed based on coinciding endonuclease restriction sites which in turn could be used to create cleaved
amplified polymorphic sequence (CAPS) markers [65].
Furthermore, functional markers could be created
directly from the significant SNPs. These markers can
thus serve as powerful tools for MAS in sorghum to
identify accessions or segregants having specific sensitivities to photoperiod.
Methods
Plant material and phenotypic evaluation
Our study was based on 219 inbred accessions of sorghum mainly of the Guinea race (additional file 1),
which were grown at the International Crops Research
Institute for the Semi-Arid Tropics (ICRISAT) research
station at Samanko, Mali in 2007 [47]. The entire panel
of accessions was shown on two dates (10 th June and
10th of July, respectively) flanking the summer solstice,
with two replications each. DFL50% was recorded for
each plot as the date when 50% of the plants had at
least half of the panicle in anthesis (Table 1). Plant
heights of the accessions were measured for both sowings dates. The photoperiod response index (PRI) for
each accession was considered as the number of days
difference in mean DFL50% between the 1st sowing and
2nd sowing and was calculated using following formula:
PRI = DFL50% 1 − DFL50% 2
where DFL50%1 and DFL50%2 are the mean days to
50% flowering observed for the first sowing date and
second sowing date, respectively, with all DFL50% values
expressed in days after sowing. Values close to zero
indicate non-photoperiod-sensitive flowering (stable
vegetative period); values close to 30 (the difference
between the first and second sowing dates) or even
Bhosale et al. BMC Plant Biology 2012, 12:32
http://www.biomedcentral.com/1471-2229/12/32
higher indicate high sensitivity to photoperiod (sharp
shortening of the vegetative period with the later sowing
date, and its associated shorter photoperiods).
Candidate gene sequencing
Primers were designed for the desired regions in CGs
CRY1 (fragment designation: CRY1-b1), CRY2 (fragment
designation: CRY2-2), LHY (fragment designation: LHY-4),
GI, HD6, and SbD8 [66] based on sequences published in
public databases (NCBI and Phytozome) using Primer Premier Software (Premier Biosoft International, Palo Alto,
CA, USA). The amplified regions of the CGs were selected
on the basis of best possible primer combinations (with
minimum secondary structures such as primer dimers and
hairpins) and with optimum product size. Primer
sequences and their respective melting temperatures are
given in Table 3. Besides these six CGs, primers were also
designed for partial amplification of other potentially
important photoperiod genes such as Phytochrome A, B
and C. Because of the lack of polymorphisms within the
amplified fragments, these genes were not considered for
further analysis. PCR reactions were performed and PCR
products were sequenced by QIAGEN (Hilden, Germany).
The gene fragments were sequenced by an easy read
sequencing service using ABI BigDye Terminator 3.1
chemistry on a capillary automatic sequencing device
(3730xl ABI 96; Applied Biosystems/Applera, Darmstadt,
Germany). In our study, the best sequencing results by the
easy read sequencing service were obtained for the fragment sizes ± 800 base pairs. Therefore, the CGs included
in our study were partially amplified to fit in this range.
The sequences obtained were manually checked for allele
calling errors and edited manually by using software Chromas [67]. The gained nucleotide sequence data were
deposited in the NCBI GenBank under the following
accession numbers: CRYPTOCHROME 1 (CRY1; Sb04g0
23680; [NCBI GenBank accession number: JQ350839]),
CRYPTOCHROME 2 (CRY2; Sb06g018510; [GenBank:
JQ350840]), LATE ELONGATED HYPOCOTYL (LHY;
Sb04g031590; [GenBank: JQ350844]), GIGANTEA (GI;
Sb03g003650;[GenBank: JQ350842]), HEADING DATE 6
(HD6; Sb02g001110; [GenBank: JQ350843]), and Dwarf8
(SbD8; Sb01g010660; [GenBank: JQ350841]).
For further analysis of the sequenced genes, multiple
alignments of the sequences were done by using software
program ClustalW2 [68]. For CGs, the number of polymorphic sites (S), pairwise nucleotide diversity (π), and
Tajima’s D [69] values, were computed using DnaSP [70].
For the CGs, the linkage disequilibrium matrix plots (Figure 2 and additional file 2) of r 2 (squared correlation
coefficient) values against the pair-wise physical distance
between polymorphic sites were obtained with software
TASSEL [71].
Page 8 of 10
Association analyses
The population structure of the diversity panel was previously determined by the software STRUCTURE (Bhosale et al. [47]) and its Q matrix employed herein for
association analysis. This was done by setting the number
of subgroups from 1 to 20 with five runs, allowing for the
admixture, correlated allele frequencies and no recombination information. For each run of STRUCTURE, the
burn-in time as well as the iteration number for the Markov chain Monte Carlo algorithm was set to 100,000.
The QK method described by Yu et al. [48] was used
for detection of marker-phenotype associations:
Mip = μ +
Qiu vu + ap + gi + eip
where Mip is the adjusted entry mean of the ith sorghum inbred carrying the p th allele, μ is an intercept
term, vu the effect of the uth column of the population
structure matrix Q, a p the effect of allele p, g i the
genetic effect of the ith sorghum inbred in addition to
ap, and eip is the residual [49]. The variances of the random effects g = {g1, ... g219} and e = {e1, 1,..., e209, 2} were
assumed to be var (g) = 2Kσg2 and var (e) = Iσr2 , where
K was a 219 × 219 matrix of kinship coefficients that
define the degree of genetic covariance between all pairs
of entries and was calculated using SPAGeDi [72]. Estimates of σg2 , the genetic variance and σr2 , the residual
variance, were obtained by REML. All mixed-model calculations were performed with ASReml release 2.0 [73].
Additional material
Additional file 1: List of sorghum accessions, days to 50% flowering
(DFL50%) for June and July sowings, their photoperiod response
indices (PRIs), races and countries of origin.
Additional file 2: Amplified fragments of sorghum candidate genes
blasted against sorghum genome database.
Additional file 3: Strength and extent of linkage disequilibrium for
genes CRY2-2, SbD8, HD6, and LHY4. Each point in the linkage
disequilibrium matrix represents a comparison between a pair of
polymorphic sites, with the r2 values displayed above the diagonal, and P
values for Fisher’s exact test below.
Additional file 4: Association of genes CRY1-b1, CRY2-2, SbD8, GI,
HD6, and LHY4 with photoperiod response index (PRI) in sorghum.
List of abbreviations
PRI: Photoperiod response index; SNPs: Single nucleotide polymorphisms;
CRY1-b1: CRYPTOCHROME 1; GI: GIGANTEA; WCA: Western and Central Africa;
SD: Short-day; LD: Long-day; CCA1: CIRCADIAN CLOCK ASSOCIATED1; LHY:
LATE ELONGATED HYPOCOTYL; TOC1: TIMING OF CAB EXPRESSION1; CO:
CONSTANS; FT: FLOWERING LOCUS T; CGs: Candidate genes; HD6: HEADING
DATE 6; SbD8: Dwarf8; DFL50%: Days to 50% flowering; UTR: Untranslated
region; FAD: Flavin adenine dinucleotide; CAPS: Cleaved amplified
polymorphic sequence; ICRISAT: International Crops Research Institute for the
Semi-Arid Tropics.
Bhosale et al. BMC Plant Biology 2012, 12:32
http://www.biomedcentral.com/1471-2229/12/32
Acknowledgements
This research was funded by the Federal Ministry for Economic Cooperation
and Development, Germany (ICRISAT/GTZ Project No. 05.7860.9-001.00), with
additional support from the United Sorghum Checkoff Program (USA, to A.H.
P.) and the CGIAR Generation Challenge Program (to AHP and CTH). The
authors sincerely thank the team at ICRISAT-Mali for successfully conducting
the sorghum field trial and for the data collection, and the laboratory staff at
University of Hohenheim for their continuous assistance during the entire
molecular work of this project. The authors also thank Yves Vigouroux for
providing HD6 primers. This paper is dedicated to the memory of Dr. Heiko K.
Parzies (1959-2011) who saw through to the completion of the project.
Author details
Institute of Plant Breeding, Seed Science, and Population Genetics,
University of Hohenheim, 70593 Stuttgart, Germany. 2Max Planck Institute for
Plant Breeding Research, 50829 Köln, Germany. 3International Crops Research
Institute for the Semi-Arid Tropics (ICRISAT) - Bamako, BP 320 Bamako, Mali.
4
ICRISAT - Sadoré, BP 12404 Niamey, Niger. 5ICRISAT - Patancheru,
Hyderabad 502324, Andhra Pradesh, India. 6Plant Genome Mapping
Laboratory, University of Georgia, Athens GA 30602, USA. 7U.S. Dept. of
Agriculture, Agricultural Research Service, Tropical Agriculture Research
Station, 2200 P.A. Campos Ave., Mayaguez P.R. 00680, Puerto Rico.
1
Authors’ contributions
HKP, BIGH designed and HKP and AEM supervised the research; FR and EW
conducted the field trials, SUB conducted the molecular work and BS and
SUB analyzed the data. PR, CTH, AP and HC identified locations of CGs in
the sorghum genome and extensively revised the manuscript. SUB, HKP, BS,
BIGH, PR, CTH and FR wrote the manuscript. All authors except HKP read
and approved the final manuscript.
Competing interests
The authors of the manuscript entitled ‘Association analysis of photoperiodic
flowering time genes in west and central African sorghum [sorghum bicolor
(L.) Moench]’, declare that they have no competing interests.
Received: 17 June 2011 Accepted: 7 March 2012
Published: 7 March 2012
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doi:10.1186/1471-2229-12-32
Cite this article as: Bhosale et al.: Association analysis of photoperiodic
flowering time genes in west and central African sorghum [Sorghum
bicolor (L.) Moench]. BMC Plant Biology 2012 12:32.
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